diff --git a/HugeCTR/src/hps/embedding_cache.cpp b/HugeCTR/src/hps/embedding_cache.cpp
index 962f678b6a..f366b0fc26 100644
--- a/HugeCTR/src/hps/embedding_cache.cpp
+++ b/HugeCTR/src/hps/embedding_cache.cpp
@@ -296,7 +296,7 @@ void EmbeddingCache<TypeHashKey>::lookup(size_t const table_id, float* const d_v
     HCTR_LIB_THROW(
         cudaMemcpyAsync(d_vectors, workspace_handler.h_missing_emb_vec_[table_id],
                         num_keys * cache_config_.embedding_vec_size_[table_id] * sizeof(float),
-                        cudaMemcpyHostToDevice, stream));
+                        cudaMemcpyDefault, stream));
     HCTR_LIB_THROW(cudaStreamSynchronize(stream));
     parameter_server_->free_buffer(memory_block);
   }
@@ -552,12 +552,15 @@ void EmbeddingCache<TypeHashKey>::finalize() {
 template <typename TypeHashKey>
 void EmbeddingCache<TypeHashKey>::insert_stream_for_sync(
     std::vector<cudaStream_t> lookup_streams_) {
-  if (lookup_streams_.size() != gpu_emb_caches_.size()) {
-    HCTR_OWN_THROW(Error_t::WrongInput,
-                   "The number of lookup streams is not equal to the number of embedding tables.");
-  }
-  for (size_t idx = 0; idx < lookup_streams_.size(); ++idx) {
-    gpu_emb_caches_[idx]->Record(lookup_streams_[idx]);
+  if (cache_config_.use_gpu_embedding_cache_) {
+    if (lookup_streams_.size() != gpu_emb_caches_.size()) {
+      HCTR_OWN_THROW(
+          Error_t::WrongInput,
+          "The number of lookup streams is not equal to the number of embedding tables.");
+    }
+    for (size_t idx = 0; idx < lookup_streams_.size(); ++idx) {
+      gpu_emb_caches_[idx]->Record(lookup_streams_[idx]);
+    }
   }
 }
 
diff --git a/test/inference/hps/lookup_session_test.py b/test/inference/hps/lookup_session_test.py
index 53ff13f2da..2875557056 100644
--- a/test/inference/hps/lookup_session_test.py
+++ b/test/inference/hps/lookup_session_test.py
@@ -146,6 +146,13 @@ def hps_dlpack(model_name, embedding_file_list, data_file, enable_cache, cache_t
         True,
         hugectr.inference.EmbeddingCacheType_t.Dynamic,
     )
+    h1, h2 = hps_dlpack(
+        model_name,
+        embedding_file_list,
+        data_file,
+        False,
+        hugectr.inference.EmbeddingCacheType_t.Dynamic,
+    )
     u1, u2 = hps_dlpack(
         model_name, embedding_file_list, data_file, True, hugectr.inference.EmbeddingCacheType_t.UVM
     )
@@ -173,15 +180,28 @@ def hps_dlpack(model_name, embedding_file_list, data_file, enable_cache, cache_t
     diff = u2.reshape(1, 26 * 16) - d2.reshape(1, 26 * 16)
     if diff.mean() > 1e-3:
         raise RuntimeError(
-            "The lookup results of UVM cache are consistent with Dynamic cache: {}".format(
+            "The lookup results of UVM cache are not consistent with Dynamic cache: {}".format(
+                diff.mean()
+            )
+        )
+        sys.exit(1)
+    else:
+        print(
+            "The lookup results on UVM are consistent with Dynamic cache, mse: {}".format(
+                diff.mean()
+            )
+        )
+    diff = h2.reshape(1, 26 * 16) - d2.reshape(1, 26 * 16)
+    if diff.mean() > 1e-3:
+        raise RuntimeError(
+            "The lookup results of Database backend are not consistent with Dynamic cache: {}".format(
                 diff.mean()
             )
         )
         sys.exit(1)
     else:
         print(
-            "Pytorch dlpack on cpu  results are consistent with native HPS lookup api, mse: {}".format(
+            "The lookup results on Database backend are consistent with Dynamic cache, mse: {}".format(
                 diff.mean()
             )
         )
-    # hps_dlpack(model_name, network_file, dense_file, embedding_file_list, data_file, False)